Automatica (Journal of IFAC)
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
Stochastic consensus over noisy networks with Markovian and arbitrary switches
Automatica (Journal of IFAC)
Brief paper: Distributed averaging on digital erasure networks
Automatica (Journal of IFAC)
Distributed consensus for multi-agent systems with delays and noises in transmission channels
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief paper: Stochastic consensus seeking with communication delays
Automatica (Journal of IFAC)
Impulsive consensus algorithms for second-order multi-agent networks with sampled information
Automatica (Journal of IFAC)
Distributed Consensus for Multiagent Systems with Communication Delays and Limited Data Rate
SIAM Journal on Control and Optimization
Differentially private iterative synchronous consensus
Proceedings of the 2012 ACM workshop on Privacy in the electronic society
Automation and Remote Control
Consensus of second-order multi-agent systems via impulsive control using sampled hetero-information
Automatica (Journal of IFAC)
Continuous-time and sampled-data-based average consensus with logarithmic quantizers
Automatica (Journal of IFAC)
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This paper considers the coordination and consensus of networked agents where each agent has noisy measurements of its neighbors' states. For consensus seeking, we propose stochastic approximation-type algorithms with a decreasing step size, and introduce the notions of mean square and strong consensus. Although the decreasing step size reduces the detrimental effect of the noise, it also reduces the ability of the algorithm to drive the individual states towards each other. The key technique is to ensure a trade-off for the decreasing rate of the step size. By following this strategy, we first develop a stochastic double array analysis in a two-agent model, which leads to both mean square and strong consensus, and extend the analysis to a class of well-studied symmetric models. Subsequently, we consider a general network topology, and introduce stochastic Lyapunov functions together with the so-called direction of invariance to establish mean square consensus. Finally, we apply the stochastic Lyapunov analysis to a leader following scenario.